class_docstr: The basic residual block used in Darknet. Each ResBlock consists of two
  ConvModules and the input is added to the final output. Each ConvModule is
  composed of Conv, BN, and LeakyReLU. In YoloV3 paper, the first convLayer
  has half of the number of the filters as much as the second convLayer. The
  first convLayer has filter size of 1x1 and the second one has the filter
  size of 3x3.
  
  Args:
      in_channels (int): The input channels. Must be even.
      conv_cfg (dict): Config dict for convolution layer. Default: None.
      norm_cfg (dict): Dictionary to construct and config norm layer.
          Default: dict(type='BN', requires_grad=True)
      act_cfg (dict): Config dict for activation layer.
          Default: dict(type='LeakyReLU', negative_slope=0.1).
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code: def forward(self, x):
      residual = x
      out = self.conv1(x)
      out = self.conv2(out)
      out = out + residual
  
      return out
  
file_path: insightface/detection/scrfd/mmdet/models/backbones/darknet.py
imports: import logging
  import torch.nn
  from mmcv.cnn import ConvModule, constant_init, kaiming_init
  from mmcv.runner import load_checkpoint
  from torch.nn.modules.batchnorm import _BatchNorm
  from ..builder import BACKBONES
label_desc: Use image node_id 2 for calling the ResBlock obj's underlying member method code with example usage: obj.forward(x) and returns: out